def __init__(self):
args = self.arg_parser.parse_known_args()[0]
super(Ema, self).__init__()
self.name = 'ema'
self.min_history_ticks = 30
self.pair = self.parse_pairs(args.pairs)[0]
self.buy_sell_mode = BuySellMode.all
self.stop_loss = StopLoss(int(args.ticker_size))
def __init__(self):
args = self.arg_parser.parse_known_args()[0]
super(Emasuperprediction, self).__init__()
self.name = 'emasuperprediction'
self.min_history_ticks = 60
self.pair = self.parse_pairs(args.pairs)[0]
self.buy_sell_mode = BuySellMode.all
self.stop_loss = StopLoss(int(args.ticker_size))
self.Bought = False
self.interval = int(args.ticker_size)
self.standardizationFeatureFlag = True
self.numStudyTrial = 50
self.backTestInitialFund = 1000
self.backTestDays = 15
self.backTestSpread = 0
self.marginTrade = False
class Tcg(Base):
"""
Tcg strategy
About: Buy when close_price > ema20, sell when close_price < ema20 and below death_cross
"""
arg_parser = configargparse.get_argument_parser()
def __init__(self):
args = self.arg_parser.parse_known_args()[0]
super(Tcg, self).__init__()
self.name = 'tcg'
self.min_history_ticks = 30
self.pair = self.parse_pairs(args.pairs)[0]
self.buy_sell_mode = BuySellMode.all
self.stop_loss = StopLoss(int(args.ticker_size))
self.data = Data()
def calculate(self, look_back, wallet, tickr):
"""
Main strategy logic (the meat of the strategy)
"""
(dataset_cnt, _) = common.get_dataset_count(look_back,
self.group_by_field)
# Wait until we have enough data
if dataset_cnt < self.min_history_ticks:
print('dataset_cnt:', dataset_cnt)
return self.actions
self.actions.clear()
new_action = TradeState.none
# Calculate indicators
df = look_back.tail(self.min_history_ticks)
close = df['close'].values
df1 = df['id'].tail(n=1)
val = df1.iloc[0]
val = val.split('-')
loop_time = int(val[2])
market_pair = 'BTCUSD'
sentiment = 'Bear'
timeframe = '4h'
indicator_type = 'Bear TCG Cross'
loop_datetime = datetime.fromtimestamp(
loop_time, tz=pytz.utc).strftime('%Y-%m-%d %H:%M:%S')
alerts = self.data.load_backtest_epochs(market_pair, sentiment,
timeframe, indicator_type,
loop_datetime)
for alert_time in alerts['epochs']:
if alert_time in range(loop_time - 1800, loop_time):
print(
"+!+!+!+!+!++!+!+!+!++!+!++!+! ALERT HIT +!+!+!+!++!+!+! {} {} {} "
.format(alert_time, loop_time - 1800, loop_time))
print(datetime.fromtimestamp(alert_time).strftime('%c'))
print(datetime.fromtimestamp(loop_time - 1800).strftime('%c'))
print(datetime.fromtimestamp(loop_time).strftime('%c'))
new_action = TradeState.sell
trade_price = self.get_price(new_action, df.tail(), self.pair)
# print("trade price {}".format(trade_price)
# Get stop-loss
if new_action == TradeState.sell and self.stop_loss.calculate(close):
print(
'-------- stop-loss detected,..buying!!!!!!!!!!!!!!!!!!!!!!!! '
)
new_action = TradeState.buy
action = TradeAction(self.pair,
new_action,
amount=None,
rate=trade_price,
buy_sell_mode=self.buy_sell_mode)
self.actions.append(action)
return self.actions
class Ema(Base):
"""
Ema strategy
About: Buy when close_price > ema20, sell when close_price < ema20 and below death_cross
"""
arg_parser = configargparse.get_argument_parser()
def __init__(self):
args = self.arg_parser.parse_known_args()[0]
super(Ema, self).__init__()
self.name = 'ema'
self.min_history_ticks = 30
self.pair = self.parse_pairs(args.pairs)[0]
self.buy_sell_mode = BuySellMode.all
self.stop_loss = StopLoss(int(args.ticker_size))
def calculate(self, look_back, wallet):
"""
Main strategy logic (the meat of the strategy)
"""
(dataset_cnt, _) = common.get_dataset_count(look_back,
self.group_by_field)
# Wait until we have enough data
if dataset_cnt < self.min_history_ticks:
print('dataset_cnt:', dataset_cnt)
return self.actions
self.actions.clear()
new_action = TradeState.none
# Calculate indicators
df = look_back.tail(self.min_history_ticks)
close = df['close'].values
# ************** Calc EMA
ema5 = talib.EMA(close[-5:], timeperiod=5)[-1]
ema10 = talib.EMA(close[-10:], timeperiod=10)[-1]
ema20 = talib.EMA(close[-20:], timeperiod=20)[-1]
close_price = self.get_price(TradeState.none, df.tail(), self.pair)
print('close_price:', close_price, 'ema:', ema20)
if close_price < ema10 or close_price < ema20:
new_action = TradeState.sell
elif close_price > ema5 and close_price > ema10:
new_action = TradeState.buy
# ************** Calc EMA Death Cross
ema_interval_short = 6
ema_interval_long = 25
ema_short = talib.EMA(close[-ema_interval_short:],
timeperiod=ema_interval_short)[-1]
ema_long = talib.EMA(close[-ema_interval_long:],
timeperiod=ema_interval_long)[-1]
if ema_short <= ema_long: # If we are below death cross, sell
new_action = TradeState.sell
trade_price = self.get_price(new_action, df.tail(), self.pair)
# Get stop-loss
if new_action == TradeState.buy and self.stop_loss.calculate(close):
print('stop-loss detected,..selling')
new_action = TradeState.sell
action = TradeAction(self.pair,
new_action,
amount=None,
rate=trade_price,
buy_sell_mode=self.buy_sell_mode)
self.actions.append(action)
return self.actions